@inproceedings{xu-etal-2026-mgsm,
title = "{MGSM}-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation",
author = "Xu, Tianyi and
Uemura, Kosei and
Kondoro, Alfred Malengo and
Essuman, Catherine Nana Nyaah and
Belay, Tadesse Destaw and
Okoh, Ifeoma and
Afolabi, Ganiyat and
Awokoya, Ayodele and
Adelani, David Ifeoluwa",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.129/",
pages = "2846--2863",
ISBN = "979-8-89176-439-2",
abstract = "Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagged behind English in both difficulty and recency. Recently, GSM-Symbolic showed a strong evidence of high variance when models are evaluated on different instantiations of the same question; however, the evaluation was conducted only in English. In this paper, we introduce MGSM-Pro, an extension of MGSM dataset with GSM-Symbolic approach. Our dataset provides five instantiations per MGSM question by varying names, digits and irrelevant context. Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set. We further find that models robustness in HRL setting do not necessarily translate to LRL. Moreover, proprietary models, such as Gemini 2.5 Flash and GPT-4.1 are less robust to digit, whereas Gemini 3.0 Pro is more robust. Among open models, GPT-OSS 120B and DeepSeek v3 show stronger robustness. Based on these findings, we recommend evaluating each problem using at least five digit-varying instantiations to obtain a more robust and realistic assessment of math reasoning."
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<abstract>Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagged behind English in both difficulty and recency. Recently, GSM-Symbolic showed a strong evidence of high variance when models are evaluated on different instantiations of the same question; however, the evaluation was conducted only in English. In this paper, we introduce MGSM-Pro, an extension of MGSM dataset with GSM-Symbolic approach. Our dataset provides five instantiations per MGSM question by varying names, digits and irrelevant context. Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set. We further find that models robustness in HRL setting do not necessarily translate to LRL. Moreover, proprietary models, such as Gemini 2.5 Flash and GPT-4.1 are less robust to digit, whereas Gemini 3.0 Pro is more robust. Among open models, GPT-OSS 120B and DeepSeek v3 show stronger robustness. Based on these findings, we recommend evaluating each problem using at least five digit-varying instantiations to obtain a more robust and realistic assessment of math reasoning.</abstract>
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%0 Conference Proceedings
%T MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation
%A Xu, Tianyi
%A Uemura, Kosei
%A Kondoro, Alfred Malengo
%A Essuman, Catherine Nana Nyaah
%A Belay, Tadesse Destaw
%A Okoh, Ifeoma
%A Afolabi, Ganiyat
%A Awokoya, Ayodele
%A Adelani, David Ifeoluwa
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F xu-etal-2026-mgsm
%X Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagged behind English in both difficulty and recency. Recently, GSM-Symbolic showed a strong evidence of high variance when models are evaluated on different instantiations of the same question; however, the evaluation was conducted only in English. In this paper, we introduce MGSM-Pro, an extension of MGSM dataset with GSM-Symbolic approach. Our dataset provides five instantiations per MGSM question by varying names, digits and irrelevant context. Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set. We further find that models robustness in HRL setting do not necessarily translate to LRL. Moreover, proprietary models, such as Gemini 2.5 Flash and GPT-4.1 are less robust to digit, whereas Gemini 3.0 Pro is more robust. Among open models, GPT-OSS 120B and DeepSeek v3 show stronger robustness. Based on these findings, we recommend evaluating each problem using at least five digit-varying instantiations to obtain a more robust and realistic assessment of math reasoning.
%U https://aclanthology.org/2026.aacl-long.129/
%P 2846-2863
Markdown (Informal)
[MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation](https://aclanthology.org/2026.aacl-long.129/) (Xu et al., AACL-IJCNLP 2026)
ACL
- Tianyi Xu, Kosei Uemura, Alfred Malengo Kondoro, Catherine Nana Nyaah Essuman, Tadesse Destaw Belay, Ifeoma Okoh, Ganiyat Afolabi, Ayodele Awokoya, and David Ifeoluwa Adelani. 2026. MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2846–2863, Hengqin, China. Association for Computational Linguistics.